Overview

Brought to you by YData

Dataset statistics

 Raw_FeatBinned_Feat
Number of variables88
Number of observations56255625
Missing cells00
Missing cells (%)0.0%0.0%
Duplicate rows00
Duplicate rows (%)0.0%0.0%
Total size in memory357.2 KiB486.2 KiB
Average record size in memory65.0 B88.5 B

Variable types

 Raw_FeatBinned_Feat
Numeric77
Categorical11

Alerts

Raw_FeatBinned_Feat
churn is highly overall correlated with customer_service_callschurn is highly overall correlated with customer_service_callsHigh Correlation
customer_service_calls is highly overall correlated with churncustomer_service_calls is highly overall correlated with churnHigh Correlation
churn is highly imbalanced (57.3%) churn is highly imbalanced (57.3%) Imbalance
customer_happiness has unique values Alert not present in this datasetUnique
n_sms has 401 (7.1%) zeros n_sms has 405 (7.2%) zeros Zeros
customer_service_calls has 2813 (50.0%) zeros customer_service_calls has 2985 (53.1%) zeros Zeros
Alert not present in this datasettotal_eve_minutes has 128 (2.3%) zeros Zeros
Alert not present in this datasettotal_eve_calls has 114 (2.0%) zeros Zeros
Alert not present in this datasetcustomer_happiness has 256 (4.6%) zeros Zeros

Reproduction

 Raw_FeatBinned_Feat
Analysis started2024-08-30 11:02:25.1591032024-08-30 11:02:28.310265
Analysis finished2024-08-30 11:02:28.3059922024-08-30 11:02:31.372532
Duration3.15 seconds3.06 seconds
Software versionydata-profiling vv4.9.0ydata-profiling vv4.9.0
Download configurationconfig.jsonconfig.json

Variables

total_day_minutes
Real number (ℝ)

 Raw_FeatBinned_Feat
Distinct558017
Distinct (%)99.2%0.3%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean902.9143716.8032
 Raw_FeatBinned_Feat
Minimum00
Maximum2200.341819
Zeros4646
Zeros (%)0.8%0.8%
Negative00
Negative (%)0.0%0.0%
Memory size87.9 KiB216.9 KiB
2024-08-30T13:02:31.773159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

 Raw_FeatBinned_Feat
Minimum00
5-th percentile290.9743314
Q1654.0326216
median902.850317
Q31140.951118
95-th percentile1514.782519
Maximum2200.341819
Range2200.341819
Interquartile range (IQR)486.91852

Descriptive statistics

 Raw_FeatBinned_Feat
Standard deviation363.526912.0554693
Coefficient of variation (CV)0.402615050.12232607
Kurtosis-0.1202540935.77271
Mean902.9143716.8032
Median Absolute Deviation (MAD)244.468561
Skewness0.045672363-4.9693356
Sum5078893.394518
Variance132151.814.2249542
MonotonicityNot monotonicNot monotonic
2024-08-30T13:02:31.886829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 46
 
0.8%
395.3765748 1
 
< 0.1%
1009.842717 1
 
< 0.1%
1113.102191 1
 
< 0.1%
1306.705756 1
 
< 0.1%
823.0121292 1
 
< 0.1%
1130.40277 1
 
< 0.1%
527.3889125 1
 
< 0.1%
721.4302925 1
 
< 0.1%
1210.862443 1
 
< 0.1%
Other values (5570) 5570
99.0%
ValueCountFrequency (%)
17 1919
34.1%
18 1784
31.7%
16 952
16.9%
15 333
 
5.9%
19 316
 
5.6%
14 138
 
2.5%
13 64
 
1.1%
0 46
 
0.8%
12 42
 
0.7%
11 11
 
0.2%
Other values (7) 20
 
0.4%
ValueCountFrequency (%)
0 46
0.8%
5.661380733 1
 
< 0.1%
8.374701903 1
 
< 0.1%
8.976510403 1
 
< 0.1%
10.15815743 1
 
< 0.1%
16.42871301 1
 
< 0.1%
17.09916813 1
 
< 0.1%
20.94959089 1
 
< 0.1%
27.28350923 1
 
< 0.1%
27.38991879 1
 
< 0.1%
ValueCountFrequency (%)
0 46
0.8%
4 1
 
< 0.1%
5 2
 
< 0.1%
6 1
 
< 0.1%
7 3
 
0.1%
8 3
 
0.1%
9 2
 
< 0.1%
10 8
 
0.1%
11 11
 
0.2%
12 42
0.7%
ValueCountFrequency (%)
0 46
0.8%
4 1
 
< 0.1%
5 2
 
< 0.1%
6 1
 
< 0.1%
7 3
 
0.1%
8 3
 
0.1%
9 2
 
< 0.1%
10 8
 
0.1%
11 11
 
0.2%
12 42
0.7%
ValueCountFrequency (%)
0 46
0.8%
5.661380733 1
 
< 0.1%
8.374701903 1
 
< 0.1%
8.976510403 1
 
< 0.1%
10.15815743 1
 
< 0.1%
16.42871301 1
 
< 0.1%
17.09916813 1
 
< 0.1%
20.94959089 1
 
< 0.1%
27.28350923 1
 
< 0.1%
27.38991879 1
 
< 0.1%

n_sms
Real number (ℝ)

 Raw_FeatBinned_Feat
Distinct79440
Distinct (%)14.1%0.7%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean307.7868418.542578
 Raw_FeatBinned_Feat
Minimum00
Maximum107039
Zeros401405
Zeros (%)7.1%7.2%
Negative00
Negative (%)0.0%0.0%
Memory size87.9 KiB216.9 KiB
2024-08-30T13:02:31.998134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

 Raw_FeatBinned_Feat
Minimum00
5-th percentile00
Q11608
median29918
Q344129
95-th percentile643.837
Maximum107039
Range107039
Interquartile range (IQR)28121

Descriptive statistics

 Raw_FeatBinned_Feat
Standard deviation195.3100612.011923
Coefficient of variation (CV)0.634562740.64780219
Kurtosis-0.32444139-1.2331986
Mean307.7868418.542578
Median Absolute Deviation (MAD)14110
Skewness0.355068060.030904075
Sum1731301104302
Variance38146.021144.28628
MonotonicityNot monotonicNot monotonic
2024-08-30T13:02:32.078917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 401
 
7.1%
280 19
 
0.3%
244 18
 
0.3%
308 18
 
0.3%
228 18
 
0.3%
393 17
 
0.3%
186 17
 
0.3%
229 17
 
0.3%
348 17
 
0.3%
287 16
 
0.3%
Other values (784) 5067
90.1%
ValueCountFrequency (%)
0 405
 
7.2%
31 142
 
2.5%
2 141
 
2.5%
12 140
 
2.5%
8 140
 
2.5%
27 140
 
2.5%
23 139
 
2.5%
6 138
 
2.5%
4 137
 
2.4%
25 137
 
2.4%
Other values (30) 3966
70.5%
ValueCountFrequency (%)
0 401
7.1%
1 4
 
0.1%
2 1
 
< 0.1%
3 4
 
0.1%
4 2
 
< 0.1%
5 5
 
0.1%
6 3
 
0.1%
7 2
 
< 0.1%
8 3
 
0.1%
9 6
 
0.1%
ValueCountFrequency (%)
0 405
7.2%
1 132
 
2.3%
2 141
 
2.5%
3 128
 
2.3%
4 137
 
2.4%
5 129
 
2.3%
6 138
 
2.5%
7 132
 
2.3%
8 140
 
2.5%
9 134
 
2.4%
ValueCountFrequency (%)
0 405
7.2%
1 132
 
2.3%
2 141
 
2.5%
3 128
 
2.3%
4 137
 
2.4%
5 129
 
2.3%
6 138
 
2.5%
7 132
 
2.3%
8 140
 
2.5%
9 134
 
2.4%
ValueCountFrequency (%)
0 401
7.1%
1 4
 
0.1%
2 1
 
< 0.1%
3 4
 
0.1%
4 2
 
< 0.1%
5 5
 
0.1%
6 3
 
0.1%
7 2
 
< 0.1%
8 3
 
0.1%
9 6
 
0.1%

total_eve_minutes
Real number (ℝ)

 Raw_FeatBinned_Feat
Distinct561044
Distinct (%)99.7%0.8%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean598.2244321.499733
 Raw_FeatBinned_Feat
Minimum00
Maximum1386.625643
Zeros16128
Zeros (%)0.3%2.3%
Negative00
Negative (%)0.0%0.0%
Memory size87.9 KiB216.9 KiB
2024-08-30T13:02:32.155464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

 Raw_FeatBinned_Feat
Minimum00
5-th percentile257.141272
Q1459.7631810
median597.4202821
Q3737.1450732
95-th percentile942.8818941
Maximum1386.625643
Range1386.625643
Interquartile range (IQR)277.3818922

Descriptive statistics

 Raw_FeatBinned_Feat
Standard deviation209.0966112.701521
Coefficient of variation (CV)0.34952870.59077575
Kurtosis-0.0052023789-1.2013463
Mean598.2244321.499733
Median Absolute Deviation (MAD)138.5322911
Skewness0.0394596893.1464422 × 10-5
Sum3365012.4120936
Variance43721.392161.32864
MonotonicityNot monotonicNot monotonic
2024-08-30T13:02:32.237570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16
 
0.3%
607.6416757 1
 
< 0.1%
253.2963518 1
 
< 0.1%
776.9752521 1
 
< 0.1%
634.9812473 1
 
< 0.1%
375.8829097 1
 
< 0.1%
778.7847026 1
 
< 0.1%
599.6714593 1
 
< 0.1%
450.9510696 1
 
< 0.1%
447.4386158 1
 
< 0.1%
Other values (5600) 5600
99.6%
ValueCountFrequency (%)
21 128
 
2.3%
25 128
 
2.3%
39 128
 
2.3%
23 128
 
2.3%
1 128
 
2.3%
4 128
 
2.3%
26 128
 
2.3%
41 128
 
2.3%
2 128
 
2.3%
42 128
 
2.3%
Other values (34) 4345
77.2%
ValueCountFrequency (%)
0 16
0.3%
0.1377191373 1
 
< 0.1%
9.504674031 1
 
< 0.1%
12.81616532 1
 
< 0.1%
15.71273512 1
 
< 0.1%
17.53084078 1
 
< 0.1%
21.28808554 1
 
< 0.1%
26.29539222 1
 
< 0.1%
30.54955884 1
 
< 0.1%
41.18032332 1
 
< 0.1%
ValueCountFrequency (%)
0 128
2.3%
1 128
2.3%
2 128
2.3%
3 128
2.3%
4 128
2.3%
5 127
2.3%
6 128
2.3%
7 128
2.3%
8 128
2.3%
9 128
2.3%
ValueCountFrequency (%)
0 128
2.3%
1 128
2.3%
2 128
2.3%
3 128
2.3%
4 128
2.3%
5 127
2.3%
6 128
2.3%
7 128
2.3%
8 128
2.3%
9 128
2.3%
ValueCountFrequency (%)
0 16
0.3%
0.1377191373 1
 
< 0.1%
9.504674031 1
 
< 0.1%
12.81616532 1
 
< 0.1%
15.71273512 1
 
< 0.1%
17.53084078 1
 
< 0.1%
21.28808554 1
 
< 0.1%
26.29539222 1
 
< 0.1%
30.54955884 1
 
< 0.1%
41.18032332 1
 
< 0.1%

total_eve_calls
Real number (ℝ)

 Raw_FeatBinned_Feat
Distinct31550
Distinct (%)5.6%0.9%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean149.5646224.370844
 Raw_FeatBinned_Feat
Minimum00
Maximum38649
Zeros36114
Zeros (%)0.6%2.0%
Negative00
Negative (%)0.0%0.0%
Memory size87.9 KiB216.9 KiB
2024-08-30T13:02:32.323231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

 Raw_FeatBinned_Feat
Minimum00
5-th percentile512
Q111012
median15024
Q319037
95-th percentile24547
Maximum38649
Range38649
Interquartile range (IQR)8025

Descriptive statistics

 Raw_FeatBinned_Feat
Standard deviation58.96637114.430885
Coefficient of variation (CV)0.394253460.59213724
Kurtosis-0.064082145-1.1987536
Mean149.5646224.370844
Median Absolute Deviation (MAD)4013
Skewness0.045497220.0080429717
Sum841301137086
Variance3477.0329208.25043
MonotonicityNot monotonicNot monotonic
2024-08-30T13:02:32.505459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126 48
 
0.9%
165 46
 
0.8%
167 46
 
0.8%
190 45
 
0.8%
177 44
 
0.8%
144 44
 
0.8%
158 43
 
0.8%
157 43
 
0.8%
174 43
 
0.8%
140 42
 
0.7%
Other values (305) 5181
92.1%
ValueCountFrequency (%)
16 145
 
2.6%
23 138
 
2.5%
12 135
 
2.4%
18 131
 
2.3%
39 131
 
2.3%
9 128
 
2.3%
5 128
 
2.3%
27 127
 
2.3%
37 126
 
2.2%
8 123
 
2.2%
Other values (40) 4313
76.7%
ValueCountFrequency (%)
0 36
0.6%
2 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 2
 
< 0.1%
8 4
 
0.1%
9 3
 
0.1%
10 3
 
0.1%
11 5
 
0.1%
ValueCountFrequency (%)
0 114
2.0%
1 119
2.1%
2 113
2.0%
3 116
2.1%
4 110
2.0%
5 128
2.3%
6 90
1.6%
7 110
2.0%
8 123
2.2%
9 128
2.3%
ValueCountFrequency (%)
0 114
2.0%
1 119
2.1%
2 113
2.0%
3 116
2.1%
4 110
2.0%
5 128
2.3%
6 90
1.6%
7 110
2.0%
8 123
2.2%
9 128
2.3%
ValueCountFrequency (%)
0 36
0.6%
2 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 2
 
< 0.1%
8 4
 
0.1%
9 3
 
0.1%
10 3
 
0.1%
11 5
 
0.1%

customer_service_rating
Real number (ℝ)

 Raw_FeatBinned_Feat
Distinct114
Distinct (%)0.2%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean7.41866672.3457778
 Raw_FeatBinned_Feat
Minimum00
Maximum103
Zeros338
Zeros (%)0.1%0.7%
Negative00
Negative (%)0.0%0.0%
Memory size87.9 KiB216.9 KiB
2024-08-30T13:02:32.571313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

 Raw_FeatBinned_Feat
Minimum00
5-th percentile41
Q162
median83
Q393
95-th percentile103
Maximum103
Range103
Interquartile range (IQR)31

Descriptive statistics

 Raw_FeatBinned_Feat
Standard deviation1.87429650.75648714
Coefficient of variation (CV)0.2526460.32248884
Kurtosis-0.26665721-0.54367858
Mean7.41866672.3457778
Median Absolute Deviation (MAD)10
Skewness-0.47541531-0.76182157
Sum4173013195
Variance3.51298720.5722728
MonotonicityNot monotonicNot monotonic
2024-08-30T13:02:32.621812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
8 1084
19.3%
7 1026
18.2%
10 900
16.0%
9 896
15.9%
6 822
14.6%
5 489
8.7%
4 265
 
4.7%
3 105
 
1.9%
2 25
 
0.4%
1 10
 
0.2%
ValueCountFrequency (%)
3 2880
51.2%
2 1848
32.9%
1 859
 
15.3%
0 38
 
0.7%
ValueCountFrequency (%)
0 3
 
0.1%
1 10
 
0.2%
2 25
 
0.4%
3 105
 
1.9%
4 265
 
4.7%
5 489
8.7%
6 822
14.6%
7 1026
18.2%
8 1084
19.3%
9 896
15.9%
ValueCountFrequency (%)
0 38
 
0.7%
1 859
 
15.3%
2 1848
32.9%
3 2880
51.2%
ValueCountFrequency (%)
0 38
 
0.7%
1 859
 
15.3%
2 1848
32.9%
3 2880
51.2%
ValueCountFrequency (%)
0 3
 
0.1%
1 10
 
0.2%
2 25
 
0.4%
3 105
 
1.9%
4 265
 
4.7%
5 489
8.7%
6 822
14.6%
7 1026
18.2%
8 1084
19.3%
9 896
15.9%

customer_happiness
Real number (ℝ)

 Raw_FeatBinned_Feat
Distinct562520
Distinct (%)100.0%0.4%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.504660529.5928889
 Raw_FeatBinned_Feat
Minimum0.000179391010
Maximum0.9998687119
Zeros0256
Zeros (%)0.0%4.6%
Negative00
Negative (%)0.0%0.0%
Memory size87.9 KiB216.9 KiB
2024-08-30T13:02:32.684242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

 Raw_FeatBinned_Feat
Minimum0.000179391010
5-th percentile0.0536147481
Q10.253094375
median0.5105657710
Q30.7576699515
95-th percentile0.9514103919
Maximum0.9998687119
Range0.9996893219
Interquartile range (IQR)0.5045755810

Descriptive statistics

 Raw_FeatBinned_Feat
Standard deviation0.290661985.8031929
Coefficient of variation (CV)0.575955450.60494737
Kurtosis-1.2269888-1.2325196
Mean0.504660529.5928889
Median Absolute Deviation (MAD)0.252789635
Skewness-0.0096845972-0.011489851
Sum2838.715453960
Variance0.08448438633.677048
MonotonicityNot monotonicNot monotonic
2024-08-30T13:02:32.816660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.183440548 1
 
< 0.1%
0.1182925103 1
 
< 0.1%
0.5159505222 1
 
< 0.1%
0.6489132417 1
 
< 0.1%
0.5419121075 1
 
< 0.1%
0.6339349819 1
 
< 0.1%
0.582825159 1
 
< 0.1%
0.9452413572 1
 
< 0.1%
0.8098898999 1
 
< 0.1%
0.6851322974 1
 
< 0.1%
Other values (5615) 5615
99.8%
ValueCountFrequency (%)
5 339
 
6.0%
18 312
 
5.5%
16 305
 
5.4%
1 298
 
5.3%
19 291
 
5.2%
14 287
 
5.1%
13 286
 
5.1%
11 286
 
5.1%
3 282
 
5.0%
10 279
 
5.0%
Other values (10) 2660
47.3%
ValueCountFrequency (%)
0.0001793910098 1
< 0.1%
0.0002135787151 1
< 0.1%
0.0004282800256 1
< 0.1%
0.0007159080322 1
< 0.1%
0.001564124626 1
< 0.1%
0.001667041497 1
< 0.1%
0.001753145611 1
< 0.1%
0.002242134096 1
< 0.1%
0.002280608135 1
< 0.1%
0.002336968566 1
< 0.1%
ValueCountFrequency (%)
0 256
4.6%
1 298
5.3%
2 276
4.9%
3 282
5.0%
4 270
4.8%
5 339
6.0%
6 252
4.5%
7 269
4.8%
8 263
4.7%
9 255
4.5%
ValueCountFrequency (%)
0 256
4.6%
1 298
5.3%
2 276
4.9%
3 282
5.0%
4 270
4.8%
5 339
6.0%
6 252
4.5%
7 269
4.8%
8 263
4.7%
9 255
4.5%
ValueCountFrequency (%)
0.0001793910098 1
< 0.1%
0.0002135787151 1
< 0.1%
0.0004282800256 1
< 0.1%
0.0007159080322 1
< 0.1%
0.001564124626 1
< 0.1%
0.001667041497 1
< 0.1%
0.001753145611 1
< 0.1%
0.002242134096 1
< 0.1%
0.002280608135 1
< 0.1%
0.002336968566 1
< 0.1%

customer_service_calls
Real number (ℝ)

 Raw_FeatBinned_Feat
Distinct946
Distinct (%)1.7%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean11.9969781.5424
 Raw_FeatBinned_Feat
Minimum00
Maximum1115
Zeros28132985
Zeros (%)50.0%53.1%
Negative00
Negative (%)0.0%0.0%
Memory size87.9 KiB216.9 KiB
2024-08-30T13:02:32.889618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

 Raw_FeatBinned_Feat
Minimum00
5-th percentile00
Q100
median00
Q3203
95-th percentile504
Maximum1115
Range1115
Interquartile range (IQR)203

Descriptive statistics

 Raw_FeatBinned_Feat
Standard deviation17.5330871.7869319
Coefficient of variation (CV)1.46145861.1585398
Kurtosis2.5048174-1.3635539
Mean11.9969781.5424
Median Absolute Deviation (MAD)00
Skewness1.65744380.52589399
Sum674838676
Variance307.409133.1931255
MonotonicityNot monotonicNot monotonic
2024-08-30T13:02:32.955726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2813
50.0%
1 100
 
1.8%
3 89
 
1.6%
6 88
 
1.6%
15 80
 
1.4%
5 80
 
1.4%
7 72
 
1.3%
2 72
 
1.3%
12 71
 
1.3%
10 71
 
1.3%
Other values (84) 2089
37.1%
ValueCountFrequency (%)
0 2985
53.1%
4 947
 
16.8%
3 835
 
14.8%
2 445
 
7.9%
5 270
 
4.8%
1 143
 
2.5%
ValueCountFrequency (%)
0 2813
50.0%
1 100
 
1.8%
2 72
 
1.3%
3 89
 
1.6%
4 54
 
1.0%
5 80
 
1.4%
6 88
 
1.6%
7 72
 
1.3%
8 69
 
1.2%
9 65
 
1.2%
ValueCountFrequency (%)
0 2985
53.1%
1 143
 
2.5%
2 445
 
7.9%
3 835
 
14.8%
4 947
 
16.8%
5 270
 
4.8%
ValueCountFrequency (%)
0 2985
53.1%
1 143
 
2.5%
2 445
 
7.9%
3 835
 
14.8%
4 947
 
16.8%
5 270
 
4.8%
ValueCountFrequency (%)
0 2813
50.0%
1 100
 
1.8%
2 72
 
1.3%
3 89
 
1.6%
4 54
 
1.0%
5 80
 
1.4%
6 88
 
1.6%
7 72
 
1.3%
8 69
 
1.2%
9 65
 
1.2%

churn
Categorical

 Raw_FeatBinned_Feat
Distinct22
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size49.6 KiB178.6 KiB
0
5135 
1
 
490
0
5135 
1
 
490

Length

 Raw_FeatBinned_Feat
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 Raw_FeatBinned_Feat
Total characters56255625
Distinct characters22
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 Raw_FeatBinned_Feat
Unique00 ?
Unique (%)0.0%0.0%

Sample

 Raw_FeatBinned_Feat
1st row00
2nd row00
3rd row00
4th row11
5th row00

Common Values

ValueCountFrequency (%)
0 5135
91.3%
1 490
 
8.7%
ValueCountFrequency (%)
0 5135
91.3%
1 490
 
8.7%

Length

2024-08-30T13:02:33.014635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Raw_Feat

2024-08-30T13:02:33.058193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:33.096261image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5135
91.3%
1 490
 
8.7%
ValueCountFrequency (%)
0 5135
91.3%
1 490
 
8.7%

Most occurring characters

ValueCountFrequency (%)
0 5135
91.3%
1 490
 
8.7%
ValueCountFrequency (%)
0 5135
91.3%
1 490
 
8.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5625
100.0%
ValueCountFrequency (%)
(unknown) 5625
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5135
91.3%
1 490
 
8.7%
ValueCountFrequency (%)
0 5135
91.3%
1 490
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5625
100.0%
ValueCountFrequency (%)
(unknown) 5625
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5135
91.3%
1 490
 
8.7%
ValueCountFrequency (%)
0 5135
91.3%
1 490
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5625
100.0%
ValueCountFrequency (%)
(unknown) 5625
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5135
91.3%
1 490
 
8.7%
ValueCountFrequency (%)
0 5135
91.3%
1 490
 
8.7%

Interactions

Raw_Feat

2024-08-30T13:02:27.792596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:30.838105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:25.341940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:28.481163image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:25.903854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:28.839971image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:26.290954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:29.239710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:26.638827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:29.629639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:27.069522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:29.967960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:27.454977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:30.448525image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:27.839260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:30.889613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:25.485185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:28.536668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:25.949380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:28.892803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:26.338672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:29.291272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:26.687022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:29.681308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:27.115565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:30.105905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:27.500936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:30.504433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:27.885313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:30.940416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:25.565869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:28.590769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:25.993754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:28.944382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:26.386055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:29.342095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:26.735280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:29.730704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:27.160900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:30.157951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:27.546839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:30.565214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:27.936049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:30.986194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:25.644197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:28.636809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:26.043994image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:28.991632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:26.438511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:29.441028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:26.868706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:29.776571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:27.234260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:30.204263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:27.598395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:30.632543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:27.987750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:31.032066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:25.711078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:28.687402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:26.095394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:29.039390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:26.491824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:29.486688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:26.921870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:29.821732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:27.313830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:30.251228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:27.650553image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:30.685125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:28.035625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:31.080919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:25.758058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:28.737982image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:26.141120image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:29.093856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:26.541063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:29.533809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:26.971076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:29.870255image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:27.359883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:30.301140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:27.699184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:30.735391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:28.082892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:31.132217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:25.858409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:28.790454image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:26.188091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:29.191919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:26.590071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:29.584430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:27.020978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:29.921968image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:27.407899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:30.399093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

2024-08-30T13:02:27.744913image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:30.789720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

Raw_Feat

2024-08-30T13:02:33.129492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Binned_Feat

2024-08-30T13:02:33.201524image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Raw_Feat

churncustomer_happinesscustomer_service_callscustomer_service_ratingn_smstotal_day_minutestotal_eve_callstotal_eve_minutes
churn1.0000.2390.5890.2210.0190.0100.0070.023
customer_happiness0.2391.000-0.005-0.025-0.0110.0240.0140.002
customer_service_calls0.589-0.0051.0000.0270.004-0.004-0.001-0.004
customer_service_rating0.221-0.0250.0271.0000.011-0.007-0.002-0.033
n_sms0.019-0.0110.0040.0111.0000.034-0.0090.015
total_day_minutes0.0100.024-0.004-0.0070.0341.000-0.0120.001
total_eve_calls0.0070.014-0.001-0.002-0.009-0.0121.000-0.007
total_eve_minutes0.0230.002-0.004-0.0330.0150.001-0.0071.000

Binned_Feat

churncustomer_happinesscustomer_service_callscustomer_service_ratingn_smstotal_day_minutestotal_eve_callstotal_eve_minutes
churn1.0000.2390.6110.2210.0180.0080.0170.020
customer_happiness0.2391.000-0.003-0.023-0.0100.0220.0150.003
customer_service_calls0.611-0.0031.0000.0260.001-0.0050.001-0.004
customer_service_rating0.221-0.0230.0261.0000.010-0.0120.008-0.024
n_sms0.018-0.0100.0010.0101.0000.031-0.0090.015
total_day_minutes0.0080.022-0.005-0.0120.0311.000-0.0110.000
total_eve_calls0.0170.0150.0010.008-0.009-0.0111.000-0.008
total_eve_minutes0.0200.003-0.004-0.0240.0150.000-0.0081.000

Missing values

Raw_Feat

2024-08-30T13:02:28.144342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.

Binned_Feat

2024-08-30T13:02:31.195182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.

Raw_Feat

2024-08-30T13:02:28.270400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Binned_Feat

2024-08-30T13:02:31.327033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Raw_Feat

total_day_minutesn_smstotal_eve_minutestotal_eve_callscustomer_service_ratingcustomer_happinesscustomer_service_callschurn
497788395.376575221607.6416766190.18344100
69163782.477667179609.51964914430.01019500
3463421144.482697218549.21372118380.71049800
592071147.911848502559.978057163100.040356511
682551941.033109229438.06927199100.260329190
5374071123.369613739509.74056124380.55055060
9660101102.465550152525.62121310990.33366100
3447651276.746743422845.73338311760.01223410
4065461496.476813416417.09680718270.459110390
797390823.456997356764.59370424070.22113490

Binned_Feat

total_day_minutesn_smstotal_eve_minutestotal_eve_callscustomer_service_ratingcustomer_happinesscustomer_service_callschurn
49778815122233300
6916317923231000
3463421812183531400
59207183219293051
6825511713993530
5374071839144731120
96601018716123600
344765182838142000
40654618278352940
797390172334462420

Raw_Feat

total_day_minutesn_smstotal_eve_minutestotal_eve_callscustomer_service_ratingcustomer_happinesscustomer_service_callschurn
4727771072.537609488801.845927258100.89548000
440487937.321972189488.82849216290.672675370
243139583.188726258452.230233153100.54389800
557988485.329705359637.38959118670.44086800
857974945.494032546513.974808154100.40712300
914712908.61979186405.3173109390.25021400
559243606.170931194987.23325117150.94971300
77144583.781503332640.8283758480.448140130
1916121336.114596341713.00713821790.00322901
650330345.080803179625.43043425360.69700600

Binned_Feat

total_day_minutesn_smstotal_eve_minutestotal_eve_callscustomer_service_ratingcustomer_happinesscustomer_service_callschurn
4727771832364831700
4404871710132831340
2431391615102531000
557988162325362800
857974173415263800
914712174783500
5592431610423111800
7714416212563830
191612182231433001
650330159244821300

Duplicate rows

Raw_Feat

total_day_minutesn_smstotal_eve_minutestotal_eve_callscustomer_service_ratingcustomer_happinesscustomer_service_callschurn# duplicates
Dataset does not contain duplicate rows.

Binned_Feat

total_day_minutesn_smstotal_eve_minutestotal_eve_callscustomer_service_ratingcustomer_happinesscustomer_service_callschurn# duplicates
Dataset does not contain duplicate rows.